Knowledge-Based Solution Construction for Evolutionary Minimization of Systemic Risk

Abstract

This paper concerns a problem of minimizing systemic risk in a system composed of interconnected entities such as companies on the market. Systemic risk arises, when, because of an initial failure of a limited number of elements, a significant part of the system fails. The system is modelled as a graph, with some nodes in the graph initially failing. The spreading of failures can be stopped by protecting nodes in the graph, which in case of companies can be achieved by setting aside reserve funds. The goal of the optimization problem is to reduce the number of nodes that eventually fail due to connections in the system. This paper studies the possibility of utilizing external knowledge for solution construction in this problem. Rules representing reusable information are extracted from solutions of problem instances and are used when solving new instances. Experiments presented in the paper show that using rule-based knowledge representation for constructing initial population allows the evolutionary algorithm to attain better results during the optimization run.